DeepMachining: Online Prediction of Machining Errors of Lathe Machines
This work addresses the need for improved manufacturing efficiency and quality control in factories by enabling online error prediction, though it appears incremental as it applies existing deep learning techniques to a specific domain.
The paper tackles the problem of predicting machining errors in lathe machine operations by developing DeepMachining, a deep learning-based AI system that uses pretraining and fine-tuning on factory data, achieving high prediction accuracy for multiple tasks with different workpieces and cutting tools.
We describe DeepMachining, a deep learning-based AI system for online prediction of machining errors of lathe machine operations. We have built and evaluated DeepMachining based on manufacturing data from factories. Specifically, we first pretrain a deep learning model for a given lathe machine's operations to learn the salient features of machining states. Then, we fine-tune the pretrained model to adapt to specific machining tasks. We demonstrate that DeepMachining achieves high prediction accuracy for multiple tasks that involve different workpieces and cutting tools. To the best of our knowledge, this work is one of the first factory experiments using pre-trained deep-learning models to predict machining errors of lathe machines.